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CDER FDA Initiatives

CDER FDA Initiatives. Lilliam Rosario, Ph.D. . Pharmacology/Toxicology Subcommittee on Pharmacogenomics under the Advisory Committee for Pharmaceutical Sciences. CDER FDA Initiatives. A. Formation of Non-Clinical Pharmacogenomics Subcommittee B. Regulatory Research-Lab Based Initiatives

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CDER FDA Initiatives

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  1. CDER FDA Initiatives Lilliam Rosario, Ph.D. Pharmacology/Toxicology Subcommittee on Pharmacogenomics under the Advisory Committee for Pharmaceutical Sciences

  2. CDER FDA Initiatives • A. Formation of Non-Clinical Pharmacogenomics Subcommittee • B. Regulatory Research-Lab Based Initiatives • C. Collaboration with Iconix Pharmaceuticals • D. Collaboration with Expression Analysis/Schering Plough

  3. CDER FDA Initiatives • A. Formation of Non-Clinical • Pharmacogenomics Subcommittee • B. Regulatory Research-Lab Based Initiatives • C. Collaboration with Iconix Pharmaceuticals • D. Collaboration with Expression Analysis/Schering Plough

  4. Nonclinical Pharmacogenomics Subcommittee: Goals • Recommend standards for the submission and review of • nonclinical PG/TG data sets. • Develop internal consensus regarding the added value, best • interpretations, and drug development and regulatory • review implications of nonclinical PG/TG data. • Develop Center expertise and an appropriate infrastructure • to support the review of nonclinical PG/TG data. • Objectives of the committee may continue to evolve with time • (for example, proteomics and metabonomics)

  5. CDER Nonclinical Pharmacogenomics Subcommittee ODE I ODE V Patricia Harlow, Cardio-RenalPaul Brown, Dermal/Dental John Leighton,  Oncology Maria Rivera, Anti-Inflammatory Lilliam Rosario, Oncology Josie Yang, Anti-Inflammatory ODE II OTR Wafa Harrouk,Metabolic/Endocrine Frank Sistare,  Applied Pharm. Research Timothy Robison, Pulmonary Barry Rosenzweig, Applied Pharm Research Scott Pine, Applied Pharm Research ODE III Karol Thompson, Applied Pharm Research Lynnda Reid, Reproductive/Urologic Siham Biade, Imaging/Radiopharm CBER David Essayan, Clin. Pharm. Tox ODE IV Hao Zhang, Anti-Virals  Co-Chairs

  6. Nonclinical Pharmacogenomics Subcommittee: Functions • Interface with other CDER review disciplines (e.g., clinicians, statisticians) and other Centers within the Agency in recommending review standards. • Develop specific initiatives to keep committee members abreast of the latest developments in PG/TG. • Assist other subcommittees and Center groups in developing educational opportunities in PG/TG. • Provide forums for communication to regulated industry on PG/TG. • Obtain external expertise to evaluate scientific developments in PG/TG. • Provide internal expertise in evaluating nonclinical PG/TG data submissions.

  7. Nonclinical Pharmacogenomics Subcommittee: Activities • Contributed input to CDER management concerning “research information package/no regulatory impact”. • Contributed to the nonclinical section of CDER draft guidance on pharmacogenetics and pharmacogenomics. • Initiated process toward development of draft guidance on the content and format of nonclinical pharmacogenomic data submissions. • •Participated in the formation and preparations for a Pharmacology Toxicology Subcommittee on Pharmacogenomics under the Advisory Committee for Pharmaceutical Sciences (first meeting on June 10, 2003) • Participates in collaboration with Iconix Pharmaceuticals. • Participates in collaboration with Expression Analysis/Schering Plough.

  8. CDER FDA Initiatives • A. Formation of Non-Clinical Pharmacogenomics Subcommittee • B. Regulatory Research: Lab-Based Initiatives • C. Collaboration with Iconix Pharmaceuticals • D. Collaboration with Expression Analysis/Schering Plough

  9. Regulatory Research:Laboratory-Based Initiatives • Early active laboratory participants in ILSI collaborations (nephrotoxicty and genotoxicty) • Affymetrix GeneChip system collaboration - cardiotoxicity focus • Rosetta research collaboration - cardiotoxicity focus • NCTR collaborations ongoing (several biological, database, statistical, reference standards) • Schering-Plough collaboration - PBL gene expression and vasculitis

  10. FDA Office of Science & Health Coordination-Funded Collaborative Project • Genome scale expression data submitted to the FDA could be generated from a variety of microarray platforms • Oligonucleotide or cDNA-based arrays • Numerous commercial platforms • In-house custom arrays • Can a standard be developed that would help assure the FDA of the “biological truth” of the submitted microarray data (independent of platform and site of processing)?

  11. FDA Office of Science & Health Coordination-Funded Collaborative Project “Evaluation of Performance Standards and Statistical Software for Regulatory Toxicogenomic Studies” • Laboratory Component (FDA) • K. Thompson, PI @ CDER • J. Fuscoe, PI @ NCTR • Laboratory Component (Outside Collaborators) • Rosetta Inpharmatics • Agilent • NIEHS • Amgen • Iconix • Affymetrix (Statistical support) • Statistical Component • Statisticians from numerous FDA Centers

  12. GOALS Goal is to generate and evaluate a complex mixed tissue standard’s utility for assessing platform features….. • No manufacturing defects • Insignificant platform lot-to-lot variability • Assess integrity of feature location • Unambiguous consensus sequence annotation • Lack of cross-contamination in tiled probe features

  13. ….and for assessing experimental performance • Quality (integrity /purity) of starting sample • Quality of processed (labeled/amplified) sample • Hybridization performance (probe sensitivity, specificity) • Image scanning limitations (background/slope/saturation) • Transformation process into “rough” measured data (background/slope/saturation) • Normalization/scaling to an analytical value worthy of comparison • Data selection and analysis procedures to focus biological thinking (false positive/false negative minimization) • Biological conclusions that are independent of platform and represent biological truth

  14. Proposed Steps for Testing Feasibility of Mixed Tissue Standard using Benchmark Genes • Identify tissue-selective, low variance “Housekeeping” (i.e., always expressed) rat genes from control animal data in large databases. These genes should optimally exhibit a consistent rank order of expression level in defined samples (by age, sex, strain). • Select tissues with most consistent expression among control animals and most coverage of probes

  15. CDER FDA Initiatives • A. Formation of Non-Clinical Pharmacogenomics Subcommittee • B. Regulatory Research-Lab Based Initiatives • C. Collaboration with Iconix Pharmaceuticals • D. Collaboration with Expression Analysis/Schering Plough

  16. Collaboration with Iconix Pharmaceuticals • Provides research access to DrugMatrix™ system for evaluation purposes. • Provides hands-on experience using chemogenomic data and tools, including the application of molecular toxicology markers to predict drug actions. • Provides first-hand experience with a very large dataset linked to traditional toxicology outcomes. • Iconix continues to provides training and support in the areas of QA/QC methods associated with gene expression microarray data generation, analysis of data across multiple gene microarray product platforms, and the derivation and validation of markers of toxicity and mechanism from integrated chemogenomic datasets.

  17. CDER FDA Initiatives • A. Formation of Non-Clinical Pharmacogenomics Subcommittee • B. Regulatory Research-Lab Based Initiatives • C. Collaboration with Iconix Pharmaceuticals • D. Collaboration with Expression • Analysis/Schering Plough

  18. Collaboration with Expression Analysis/Schering Plough Conduct a mock submission of microarray data • Provide a suitable framework in which to augment, reduce, or further define a potential list of recommendations • Contribute to the development of consensus around the specific elements of applicable recommendations, within the context of a mock submission • Contribute to building and refining a process in which microarray data may be submitted to FDA

  19. Proposed Activity Plan for Mock Data Submission 1. Concept Definition and Refinement of Scope May 5, 2003 2. Recommendations/Aims/Approach Document Development and Review June 2003 3. Pilot Submission July 2003 4. Intermediate Stakeholder Review and Feedback August 2003 5. Incorporation of Further Refinements and Iterations Aug-Oct 2003 6. Completion of Mock Submission October 2003 7. Final Stakeholder Discussion Forum November 2003 8. Development of Summary Report November 2003

  20. Areas to Address • Laboratory infrastructure • Data management • Study-specific array performance • Study-specific experimental design • Study-specific pre-processing and statistical analysis methods • Interpretation of results

  21. Data Management • Data management, bioinformatics, and statistical analysis systems and software • Data files and file structures • Variables and definitions • Linkage mechanisms between microarray and other datasets • Histopathology • Clinical chemistry • Phenotype

  22. CDER Guidance (January 1999): Providing Regulatory Submissions in Electronic Format Animal line listings as datasets “Animal line listings that you would provide on paper or in PDF format may be provided as datasets. Just as you provide data for each domain (e.g., body weights, clinical signs) as a table in a paper or PDF submission, with electronic datasets, each domain should be provided as a single dataset”.

  23. CDER Guidance Recommendations • Provide each dataset as a SAS transport file. • Size is less than 25 MB per file (not compressed). • Data variable names should be no more than 8 characters. • A more descriptive data variable label, up to 32 characters in length, should be provided. • Data elements should be defined in data definition tables (1 set of data definition tables/study). • Each animal should be identified with a single, unique number for all the datasets in the entire application. • The variable names and codes should be consistent across studies. • Provide the duration of treatment based on the start of study treatment.

  24. CDER Guidance: Examples of data sets and data elements

  25. CDER guidance: Examples of data sets and data elements

  26. Nonclinical Data sets; Notice of Pilot Project Federal Register / Vol. 68, No. 17 / January 27, 2003 [Docket No. 02N – 0532] “This pilot project is part of an effort to improve the process for submitting nonclinical data. Eventually, FDA expects to recommend detailed data standards for the submission of nonclinical data”. FDA received recommendations for a standard presentation of certain clinical data from the Clinical Data Interchange Standards Consortium, Inc. ( CDISC). CDISC is currently facilitating the work on similar standards for nonclinical data sets.

  27. Comparison of CDER guidance to MIAME/Tox Proposal • CDER guidance paradigm appears more comprehensive with less restrictive vocabulary e.g. CDER proposal treats LABTEST as a variable, while MIAME/Tox proposes a field for each possible Clinical Chemistry test. • MIAME/Tox collects information on in vitro experiments whereas the Agency generally does not receive line listing for Pharmacology data. • MIAME/Tox did not collect information on drug plasma levels whereas toxicity studies submitted to the Agency may include PK assessments.

  28. Considerations for the submission of array data • Sponsors provide annotations to non-clinical data containing array information by following a Guidance-compliant format. • The Guidance may have to be extended to include how the array data may be submitted. • Include the following files: raw data files post image analysis (e.g., *.cel and *.chp in the case of Affymetrix array data) linked by animal identifier. • Include summary report to describe any normalizations, data processing, and/or statistical analysis; i.e., how conclusions were derived.

  29. Affymetrix MAS 5.0-Supplied Files EXP Experimental information (sample, array, fluidics/scanner). Readable in a text editor. 1KB DAT Raw image of the scanned GeneChip array. Only readable in MAS software. 40MB CEL Cell intensity file; (x,y) coordinates for each cell (i.e. probe) with the intensity of each. Can be used to re-analyze data with different expression algorithm parameters. Readable in a text editor. 10MB CHP Quantifies (signal) and qualifies (presence or absence) each transcript and its relative expression level. Text versions creatable. 10MB RPT Text file containing quality control information. 5KB 40 MB

  30. Chip Image with Defect

  31. Probe Detection Report (from CHP) Probe Set ID Stat Pairs Stat Pairs Used Signal Detec-tion P-Value Descriptions 92570_at 16 16 64.2 A 0.378184 Cluster Incl AW122482:UI-M-BH2.2-ao... 92571_at 16 16 2116.0 P 0.000266 Cluster Incl D85904:Mouse mRNA for ... 92572_at 16 16 183.0 P 0.021866 Cluster Incl AI509617:vx14h07.y1 ... 92573_at 16 16 4422.7 P 0.000266 Cluster Incl AB021743:Mus musculus ... 92574_at 16 16 1928.7 P 0.000219 Cluster Incl AI851046:UI-M-BH0-ajv-..

  32. Suggestions for the Submission of Array Data • By evaluating several submissions, we can gain understanding of the fields/issues that need to be reconciled for database purposes. This proposal, • Works with current guidance. • Does not create any additional burden for the Sponsor. • Leaves possibility of in-house database creation.

  33. CDER FDA Initiatives • A. Formation of Non-Clinical Pharmacogenomics Subcommittee • B. Regulatory Research-Lab based initiatives • C. Collaboration with Iconix • D. Collaboration with Expression Analysis/Schering Plough

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